class_docstr: MS COCO instance segmentation dataset.
  
  Parameters
  ----------
  root : str, default '~/mxnet/datasets/coco'
      Path to folder storing the dataset.
  splits : list of str, default ['instances_val2017']
      Json annotations name.
      Candidates can be: instances_val2017, instances_train2017.
  transform : callable, default None
      A function that takes data and label and transforms them. Refer to
      :doc:`./transforms` for examples.
  
      A transform function for object detection should take label into consideration,
      because any geometric modification will require label to be modified.
  min_object_area : float, default is 1
      Minimum accepted ground-truth area, if an object's area is smaller than this value,
      it will be ignored.
  skip_empty : bool, default is True
      Whether skip images with no valid object. This should be `True` in training, otherwise
      it will cause undefined behavior.
class_docstr_tok: ["MS","COCO","instance","segmentation","dataset",".","Parameters","--","--","--","--","--","root",":","str",",","default","'~\/mxnet\/datasets\/coco'","Path","to","folder","storing","the","dataset",".","splits",":","list","of","str",",","default","[","'instances_val2017","'","]","Json","annotations","name",".","Candidates","can","be",":","instances_val2017",",","instances_train2017",".","transform",":","callable",",","default","None","A","function","that","takes","data","and","label","and","transforms","them",".","Refer","to",":","doc",":","`",".\/transforms","`","for","examples",".","A","transform","function","for","object","detection","should","take","label","into","consideration",",","because","any","geometric","modification","will","require","label","to","be","modified",".","min_object_area",":","float",",","default","is","1","Minimum","accepted","ground-truth","area",",","if","an","object","'s","area","is","smaller","than","this","value",",","it","will","be","ignored",".","skip_empty",":","bool",",","default","is","True","Whether","skip","images","with","no","valid","object",".","This","should","be","`","True","`","in","training",",","otherwise","it","will","cause","undefined","behavior","."]
docstr: Return the aspect ratio of each image in the order of the raw data.
docstr_tok: ["Return","the","aspect","ratio","of","each","image","in","the","order","of","the","raw","data","."]
code: def get_im_aspect_ratio(self):
      """Return the aspect ratio of each image in the order of the raw data."""
      if self._im_aspect_ratios is not None:
          return self._im_aspect_ratios
      self._im_aspect_ratios = [None] * len(self._items)
      for i, img_path in enumerate(self._items):
          with Image.open(img_path) as im:
              w, h = im.size
              self._im_aspect_ratios[i] = 1.0 * w / h
  
      return self._im_aspect_ratios
  
file_path: gluon-cv/gluoncv/data/mscoco/instance.py
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import numpy
  from PIL import Image
  import mxnet
  from .utils import try_import_pycocotools
  from ..base import VisionDataset
label_desc: Use image node_id 5 for calling the COCOInstance obj's underlying member method code with example usage: obj.get_im_aspect_ratio() and returns: self, self